Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B–induced local skin hypersensitization in healthy subjects: a machine-learned analysis

Abstract

Machine learning identified differences in ultraviolet-B– and capsaicin-induced hypersensitization in the quantitative sensory testing parameter patterns acquired in 82 healthy subjects.

Identification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix)

“…The present implementation of the ESOM/U-matrix method as an R library may provide a broader access to such a method. Previous successful applications of this method in biomedical informatics include data sets related to pain research [43,69], computational drug discovery [40,70] or lipidomics research in neurodegenerative diseases [71].…”

Machine-learned analysis of the association of next-generation sequencing–based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin

“…Hence, a recently developed genetic panel 38 was used to address the role TRPV1 and TRPA1 genetic variants in the sensitivity to nociceptive heat and in the reaction to hypersensitization with topical capsaicin recently assessed in a cohort of healthy subjects. 46 …”

Machine learning in pain research

“…For example, after treatment of 82 subjects with local UV-B irradiation or capsaicin application and assessing the pain phenotype using 10 different QST parameters, a 246 × 10-sized data matrix was obtained in a human experimental pain study. 36 Using unsupervised machine learning implemented as emergent self-organizing maps, 71 data structures were detected that coincided with applied known treatments indicating that a modulation of the complex pain phenotype had been obtained. 36 …”

“… 36 Using unsupervised machine learning implemented as emergent self-organizing maps, 71 data structures were detected that coincided with applied known treatments indicating that a modulation of the complex pain phenotype had been obtained. 36 …”

“…The icons at the right of each main application field symbolize respective typical machine learning methods, that is, from top to bottom: (1) SVM where the grouping (classification) is obtained by placing a border (hyperplane) between classes (subsymbolic classifier), (2) a decision tree where the classification is obtained through hierarchical rules (symbolic classifier), (3) an emergent self-organizing maps as an unsupervised machine learning method able to find an interesting structure in high-dimensional data such as clusters. In this figure, the map was colored using a geographical analogy with brown (up to snow-covered) heights and green valleys, on which clusters can be separated (from Ref. 36 ).…”